Short bio: Albert Montillo obtained B.S. and M.S. degrees in computer science while minoring in electrical engineering from Rensselaer Polytechnic Institute (RPI) in 1993. From 1993 to 1997, he worked as an imaging research scientist and then as a project manager for The Analytical Sciences Coporation in Reading, MA. In 1997 he became a senior research scientist at Cognex where he developed computer vision algorithms for automated high speed computer-based inspection systems. With a growing interest in medical applications of computer vision, he then studied electrical engineering at the masters graduate level at Yale University in the Image Processing and Analysis Group.

In 1999, he began a Ph.D. program in the Vision, Analysis and Simulation Technologies lab at the University of Pennsylvania. He obtained a Ph.D. in computer science in 2004 with the thesis part of his research focused on the development of automated volumetric model reconstruction methods for tracking the human heart in 4D images, such as MRI. He worked as an Industrial Liason and Technology Manager for the Department of Radiology at the Hospital of the University of Pennsylvania and the Center for Technology Transfer, where his efforts raised $600,000 USD to further departmental medical imaging research. Currently Albert works as a research associate in the Computational Biomedicine Imaging and Modeling Center in the Biomedical Engineering Department at Rutgers University where his research is focused is on fast statistical learning methods for computer vision with an emphasis on image based regression.

Highlights: The computer vision methods Dr. Montillo developed have been awarded five patents in the United States. He received a first place paper award for his medical image processing research. Albert has had the opportunity to intern at the Harvard-MIT Center for Functional Neuroimaging Technologies, where he developed a system to learn a statistical model of human brain anatomy and label structures throughout the whole brain. This system has been shown to detect subtle morphological changes in structures that presage Alzheimer’s disease.